Model based confidence-certainty metric is a data-dependant approach derived from prior work on Dataset Cartography [1]}, and automatically identifies noisy/mislabelled samples from a dataset. Our variation to prior work combines prediction probability of ground truth and overall prediction distribution across labels to score every sample in the dataset. More precisely, we use model confidence (\(\mu \) ), where \(\mu _i\)  is the prediction probability of ground truth, and model certainty (\(\delta \) ), where \(\delta _i\)  is the difference between the prediction probability of argmax and next argmax (i.e. margin distribution) for a sample \(x_i\) . \(\mu \)  captures how confident the model is while predicting ground truth and \(\delta \)  captures how certain the model is while making a prediction. Unlike [1]}, we only use scores from the penultimate epoch of training. For each training sample \(x_i\) , we first compute \(\mu _i\)  and \(\delta _i\)  and then partition the data into segments of high/low confidence-certainty scores. The 90th percentile of datums from the least confident and most certain score range are marked as noisy/mislabelled.
